Wireless sensor networks are a fast-growing class of systems. They offer many new design challenges, due to stringent requirements like tight energy budgets, low-cost components, limited processing resources, and small footprint devices. Such strict design goals call for technologies like nanometer-scale semiconductor design and low-power wireless communication to be used. But using them would also make the sensor data more vulnerable to errors, within both the sensor nodes' hardware and the wireless communication links. Assuring the reliability of the data is going to be one of the major design challenges of future sensor networks. Traditional methods for reliability cannot always be used, because they introduce overheads at different levels, from hardware complexity to amount of data transmitted. This paper presents a new method that makes use of the properties of sensor data to enable reliable data collection. The approach consists of creating predictive models based on the temporal correlation in the data and using them for real-time error correction. This method handles multiple sources of errors together without imposing additional complexity or resource overhead at the sensor nodes. We demonstrate the ability to correct transient errors arising in sensor node hardware and wireless communication channels through simulation results on real sensor data.

A major bottleneck in satisfying the increasing demand for wireless multimedia access is the dynamic error conditions requiring a very high error protection overhead in terms of energy consumption and access delay. In this paper, we explore ways to use application-level data properties to reduce the error protection cost. We observe that the objects constituting a wireless Web-based access differ in terms of the inherent resiliency to residual error, and the object's importance to the overall content quality. Additionally, since the cost of achieving a target residual error level depends on the experienced channel conditions, the access cost can be affected by changing the transmission schedule of objects according to the channel conditions. Based on the above observations, in this paper, we present a framework that exploits the application properties, namely content importance and error resiliency, to reduce the cost of error protection by scheduling objects for transmission and adapting the lower-layer error control mechanisms based on the data properties as well as wireless channel conditions. The approach is evaluated under diverse channel conditions using a MATLAB-based simulation environment. Our results show that the proposed approach leads to significant savings in access cost (average 37.4%) with minimal degradation in application quality.

One of the main challenges in wireless sensor networks is to provide low-cost, low-energy reliable data collection. Reliability against transient errors in sensor data can be provided using the model-based error correction described in (S. Mukhopadhyay et al., Mar. 2004), in which temporal correlation in the data is used to correct errors without any overheads at the sensor nodes. In the above work it is assumed that a perfect model of the data is available. However, as variations in the physical process are context-dependent and time-varying in a real sensor network, it is infeasible to have an accurate model of the data properties a priori, thus leading to reduced correction efficiency. In this paper, we address this issue by presenting a scalable methodology for improving the accuracy of data modeling through on-line estimation and model updates. Additionally, we propose enhancements to the data correction algorithm to incorporate robustness against dynamic model changes and potential modeling errors. We evaluate our system through simulations using real sensor data collected from different sources. Experimental results demonstrate that the proposed enhancements lead to an improvement of up to a factor of 10 over the earlier approach.

One or the main challenges in adoption and deployment of wireless networked sensing applications is ensuring reliable sensor data collection and aggregation, while satisfying the low-cost, low-energy operating constraints of such applications. A wireless sensor network is inherently vulnerable to different sources of unreliability resulting in transient failures. Existing reliability techniques that address transient failures in circuits and communication channels incur prohibitively high energy, bandwidth and cost overheads in the sensor nodes. In this paper we investigate application-level error correction techniques for sensor networks that exploit the properties of sensor data to eliminate any overhead on the sensor nodes, at the expense of nominal buffer requirements at the data aggregator nodes, which are much less cost/energy constrained. Our approach involves use of spatio-temporal correlations in sensor data, the goals of the application, and its vulnerability to various errors. We present our error-correction algorithm and evaluate it through simulations using real and synthetic sensor data. Experimental results validate the feasibility of our approach to provide high degree of reliability in sensor data aggregation, without imposing overheads on sensor nodes.